Reliability of Synaptic Transmission at the Synapses of Held In Vivo under Acoustic Stimulation

BACKGROUND:The giant synapses of Held play an important role in high-fidelity auditory processing and provide a model system for synaptic transmission at central synapses. Whether transmission of action potentials can fail at these synapses has been investigated in recent studies. At the endbulbs of Held in the anteroventral cochlear nucleus (AVCN) a consistent picture emerged, whereas at the calyx of Held in the medial nucleus of the trapezoid body (MNTB) results on the reliability of transmission remain inconsistent. In vivo this discrepancy could be due to the difficulty in identifying failures of transmission. METHODS/FINDINGS:We introduce a novel method for detecting unreliable transmission in vivo. Based on the temporal relationship between a cells' waveform and other potentials in the recordings, a statistical test is developed that provides a balanced decision between the presence and the absence of failures. Its performance is quantified using simulated voltage recordings and found to exhibit a high level of accuracy. The method was applied to extracellular recordings from the synapses of Held in vivo. At the calyces of Held failures of transmission were found only rarely. By contrast, at the endbulbs of Held in the AVCN failures were found under spontaneous, excited, and suppressed conditions. In accordance with previous studies, failures occurred most abundantly in the suppressed condition, suggesting a role for inhibition. CONCLUSIONS/SIGNIFICANCE:Under the investigated activity conditions/anesthesia, transmission seems to remain largely unimpeded in the MNTB, whereas in the AVCN the occurrence of failures is related to inhibition and could be the basis/result of computational mechanisms for temporal processing. More generally, our approach provides a formal tool for studying the reliability of transmission with high statistical accuracy under typical in vivo recording conditions

TRY plant trait database - enhanced coverage and open access

Plant traits-the morphological, anatomical, physiological, biochemical and phenological characteristics of plants-determine how plants respond to environmental factors, affect other trophic levels, and influence ecosystem properties and their benefits and detriments to people. Plant trait data thus represent the basis for a vast area of research spanning from evolutionary biology, community and functional ecology, to biodiversity conservation, ecosystem and landscape management, restoration, biogeography and earth system modelling. Since its foundation in 2007, the TRY database of plant traits has grown continuously. It now provides unprecedented data coverage under an open access data policy and is the main plant trait database used by the research community worldwide. Increasingly, the TRY database also supports new frontiers of trait-based plant research, including the identification of data gaps and the subsequent mobilization or measurement of new data. To support this development, in this article we evaluate the extent of the trait data compiled in TRY and analyse emerging patterns of data coverage and representativeness. Best species coverage is achieved for categorical traits-almost complete coverage for 'plant growth form'. However, most traits relevant for ecology and vegetation modelling are characterized by continuous intraspecific variation and trait-environmental relationships. These traits have to be measured on individual plants in their respective environment. Despite unprecedented data coverage, we observe a humbling lack of completeness and representativeness of these continuous traits in many aspects. We, therefore, conclude that reducing data gaps and biases in the TRY database remains a key challenge and requires a coordinated approach to data mobilization and trait measurements. This can only be achieved in collaboration with other initiatives

Cell line-dependent variability in HIV activation employing DNMT inhibitors

Long-lived reservoirs of Human Immunodeficiency Virus (HIV) latently infected cells present the main barrier to a cure for HIV infection. Much interest has focused on identifying strategies to activate HIV, which would be used together with antiretrovirals to attack reservoirs. Several HIV activating agents, including Tumor Necrosis Factor alpha (TNFα) and other agents that activate via NF-kB are not fully effective in all latent infection models due to epigenetic restrictions, such as DNA methylation and the state of histone acetylation. DNA methyltransferases (DNMT) inhibitors like 5-aza-2'deoxycytidine (Aza-CdR) and histone deacetylase (HDAC) inhibitors like Trichostatin A (TSA) have been proposed as agents to enhance reactivation and have shown activity in model systems. However, it is not clear how the activities of DNMT and HDAC inhibitors range across different latently infected cell lines, potential models for the many different latently infected cells within an HIV patient. We determined HIV activation following treatment with TNFα, TSA and Aza-CdR across a range of well known latently infected cell lines. We assessed the activity of these compounds in four different Jurkat T cell-derived J-Lat cell lines (6.3, 8.4, 9.2 and 10.6), which have a latent HIV provirus in which GFP replaces Nef coding sequence, and ACH-2 and J1.1 (T cell-derived), and U1 (promonocyte-derived) cell lines with full-length provirus. We found that Aza-CdR plus TNFα activated HIV at least twice as well as TNFα alone for almost all J-Lat cells, as previously described, but not for J-Lat 10.6, in which TNFα plus Aza-CdR moderately decreased activation compared to TNFα alone. Surprisingly, a much greater reduction of TNFα-stimulated activation with Aza-CdR was detected for ACH-2, J1.1 and U1 cells. Reaching the highest reduction in U1 cells with a 75% reduction. Interestingly, Aza-CdR not only decreased TNFα induction of HIV expression in certain cell lines, but also decreased activation by TSA. Since DNMT inhibitors reduce the activity of provirus activators in some HIV latently infected cell lines the use of epigenetic modifying agents may need to be carefully optimized if they are to find clinical utility in therapies aimed at attacking latent HIV reservoirs

TRY plant trait database - enhanced coverage and open access

Plant traits—the morphological, anatomical, physiological, biochemical and phenological characteristics of plants—determine how plants respond to environmental factors, affect other trophic levels, and influence ecosystem properties and their benefits and detriments to people. Plant trait data thus represent the basis for a vast area of research spanning from evolutionary biology, community and functional ecology, to biodiversity conservation, ecosystem and landscape management, restoration, biogeography and earth system modelling. Since its foundation in 2007, the TRY database of plant traits has grown continuously. It now provides unprecedented data coverage under an open access data policy and is the main plant trait database used by the research community worldwide. Increasingly, the TRY database also supports new frontiers of trait‐based plant research, including the identification of data gaps and the subsequent mobilization or measurement of new data. To support this development, in this article we evaluate the extent of the trait data compiled in TRY and analyse emerging patterns of data coverage and representativeness. Best species coverage is achieved for categorical traits—almost complete coverage for ‘plant growth form’. However, most traits relevant for ecology and vegetation modelling are characterized by continuous intraspecific variation and trait–environmental relationships. These traits have to be measured on individual plants in their respective environment. Despite unprecedented data coverage, we observe a humbling lack of completeness and representativeness of these continuous traits in many aspects. We, therefore, conclude that reducing data gaps and biases in the TRY database remains a key challenge and requires a coordinated approach to data mobilization and trait measurements. This can only be achieved in collaboration with other initiatives

Harmful Elements in Estuarine and Coastal Systems

Estuaries and coastal zones are dynamic transitional systems which provide many economic and ecological benefits to humans, but also are an ideal habitat for other organisms as well. These areas are becoming contaminated by various anthropogenic activities due to a quick economic growth and urbanization. This chapter explores the sources, chemical speciation, sediment accumulation and removal mechanisms of the harmful elements in estuarine and coastal seawaters. It also describes the effects of toxic elements on aquatic flora and fauna. Finally, the toxic element pollution of the Venice Lagoon, a transitional water body located in the northeastern part of Italy, is discussed as a case study, by presenting the procedures adopted to measure the extent of the pollution, the impacts on organisms and the restoration activities

A Neuron-Glial Perspective for Computational Neuroscience

International audienceThere is growing excitement around glial cells, as compelling evidence point to new, previously unimaginable roles for these cells in information processing of the brain, with the potential to affect behavior and higher cognitive functions. Among their many possible functions, glial cells could be involved in practically every aspect of the brain physiology in health and disease. As a result, many investigators in the field welcome the notion of a Neuron-Glial paradigm of brain function, as opposed to Ramon y Cayal's more classical neuronal doctrine which identifies neurons as the prominent, if not the only, cells capable of a signaling role in the brain. The demonstration of a brain-wide Neuron-Glial paradigm however remains elusive and so does the notion of what neuron-glial interactions could be functionally relevant for the brain computational tasks. In this perspective, we present a selection of arguments inspired by available experimental and modeling studies with the aim to provide a biophysical and conceptual platform to computational neuroscience no longer as a mere prerogative of neuronal signaling but rather as the outcome of a complex interaction between neurons and glial cells

Evaluation of appendicitis risk prediction models in adults with suspected appendicitis

Background Appendicitis is the most common general surgical emergency worldwide, but its diagnosis remains challenging. The aim of this study was to determine whether existing risk prediction models can reliably identify patients presenting to hospital in the UK with acute right iliac fossa (RIF) pain who are at low risk of appendicitis. Methods A systematic search was completed to identify all existing appendicitis risk prediction models. Models were validated using UK data from an international prospective cohort study that captured consecutive patients aged 16–45 years presenting to hospital with acute RIF in March to June 2017. The main outcome was best achievable model specificity (proportion of patients who did not have appendicitis correctly classified as low risk) whilst maintaining a failure rate below 5 per cent (proportion of patients identified as low risk who actually had appendicitis). Results Some 5345 patients across 154 UK hospitals were identified, of which two‐thirds (3613 of 5345, 67·6 per cent) were women. Women were more than twice as likely to undergo surgery with removal of a histologically normal appendix (272 of 964, 28·2 per cent) than men (120 of 993, 12·1 per cent) (relative risk 2·33, 95 per cent c.i. 1·92 to 2·84; P < 0·001). Of 15 validated risk prediction models, the Adult Appendicitis Score performed best (cut‐off score 8 or less, specificity 63·1 per cent, failure rate 3·7 per cent). The Appendicitis Inflammatory Response Score performed best for men (cut‐off score 2 or less, specificity 24·7 per cent, failure rate 2·4 per cent). Conclusion Women in the UK had a disproportionate risk of admission without surgical intervention and had high rates of normal appendicectomy. Risk prediction models to support shared decision‐making by identifying adults in the UK at low risk of appendicitis were identified